Association of physical function and sleep with oxidative stress in adults with chronic pain: A pragmatic clinical trial secondary analysis

Abstract

Objective

This study aimed to evaluate the potential utility of 8-hydroxy-2ʹ-deoxyguanosine as a biomarker for pain-related functional outcomes.

Methods

This is a secondary analysis of a pragmatic clinical trial that recruited active duty service members with chronic pain. Participants underwent a 6-week interdisciplinary pain management program and completed measures of sleep-related function and physical function, including objective measures of treadmill, lift, and carry battery. Urine samples were collected at baseline and 6 weeks. We estimated partial correlations among changes in 8-hydroxy-2ʹ-deoxyguanosine, sleep-related function, and physical function, adjusting for baseline 8-hydroxy-2ʹ-deoxyguanosine and participant demographics. Given the exploratory nature of this study, we used a significance threshold of p < 0.10.

Results

Changes in 8-hydroxy-2ʹ-deoxyguanosine levels were negatively associated with changes in sleep-related function at both 3 weeks (r = −0.17; 90% confidence interval: −0.3 to −0.03) and 6 weeks (partial r = −0.24; 90% confidence interval: −0.38 to −0.1). Participants’ sleep-related function and physical function improved from baseline to 3 and 6 weeks (P < 0.001), whereas 8-hydroxy-2ʹ-deoxyguanosine levels decreased over time.

Conclusions

Among active duty service members with chronic pain, changes in physical function and sleep-related function were associated with changes in oxidative stress.

Trial registration: ClinicalTrials.gov identifier NCT03297905

Keywords

Pragmatic clinical trial chronic pain physical function sleep oxidative stress

Introduction

Chronic pain remains a persistent and complex health concern worldwide, with an even greater burden observed among military populations.^1–3 Active duty service members (ADSM) experience unique physical and psychological stressors that increase their risk of developing chronic pain, which is frequently associated with impairments in physical function, sleep deficiency, and co-occurring mental health conditions.^4,5

Maintaining optimal physical and cognitive performance is essential for military roles and readiness. Tasks such as carrying heavy loads, performing in extreme environments, and responding to emergencies require strength, endurance, mobility, and coordination. Similarly, sleep health is essential for physical recovery, emotional regulation, and cognitive functioning. Recognizing the interconnected nature of physical function and sleep, the US Army’s Holistic Health and Fitness (H2F) system has identified these two domains as core components of operational readiness and long-term health.⁶

Despite this, sleep deficiency is highly prevalent among both ADSM and veterans, with prevalence estimates ranging from 27% to 54%, which are two to three times higher than that observed in the general US population.^7–9 Department of Defense surveys conducted over the past decade have found that the majority of service members report sleeping <6 h a night, despite recommendations of ≥7 h of sleep.¹⁰ The proportion of service members with low sleep duration is even higher for those with chronic pain, which is known to be the leading cause of disability and reduced readiness in the military.¹¹

Addressing these challenges will require a deeper understanding of the biological mechanisms underlying changes in physical function and sleep.¹² One area of growing research interest is the role of oxidative stress in the pathophysiology of chronic pain.¹² Oxidative stress occurs when the production of reactive oxygen species exceeds the body’s antioxidant defenses, resulting in cellular damage.¹³ Reactive oxygen species are also known to modulate inflammatory signaling pathways and nociceptive processing, thereby contributing to pain circuit sensitization.¹⁴

Biomarkers offer a nuanced approach to understanding biological mechanisms that can inform clinical diagnosis and guide individualized therapy.¹⁵ A well-established biomarker of oxidative DNA damage (i.e. oxidative stress) is 8-hydroxy -2ʹ-deoxyguanosine (8-OHdG), which can be noninvasively measured in urine.¹⁶ Elevated urinary 8-OHdG levels have been associated with depression, fatigue, pain, and impaired physical performance.^16–18 However, the specific relationship of 8-OHdG with changes in physical function and sleep deficiency in individuals with chronic pain remains poorly understood, particularly in military populations. Therefore, this study examined the relationship between changes in urinary 8-OHdG levels and changes in physical function and sleep-related function among ADSM undergoing interdisciplinary pain treatment. By investigating these associations, we aimed to evaluate the potential utility of 8-OHdG as a biomarker for pain-related functional outcomes in a military clinical settings.

Materials and methods

Study design

This secondary analysis used data from a pragmatic clinical trial with a sequential multiple-assignment randomized trial (SMART) design that was embedded within routine clinical practice^19,20 and reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.²¹ The details of the study have been published previously. Briefly, the study was conducted at the Interdisciplinary Pain Management Center, Madigan Army Medical Center (MAMC), and the primary aim of the study was to examine the optimal treatment combination, sequence, and duration of complementary and integrative health (CIH) therapies and standard rehabilitative care (SRC) for reducing the impact of pain among ADSM. Deidentified data were used for this secondary data analysis.

In the first stage of the parent study, 280 participants were randomized to receive either SRC (consisting of physical and occupational therapy along with psychoeducation) or CIH treatments (which included chiropractic, acupuncture, yoga, and psychoeducation) for 3 weeks. Subsequent treatment during the second 3-week stage was individualized based on initial response: nonresponders were re-randomized either to switch modalities or to receive a combined approach, whereas responders continued their original therapy.¹⁹

The research protocol for this study was approved by the MAMC Institutional Review Board (protocol # 218031) on 13 December 2017. All participants provided written informed consent. The investigators adhered to the policies for protection of human participants as prescribed in 45 CFR 46. The study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2024.

Sample

The SMART study enrolled ADSM referred to an Interdisciplinary Pain Management Center (IPMC) for the treatment of chronic pain.^22,23 Participants were members of the US Army, Air Force, Navy, and Marine Corps. Each participant completed self-reported assessments at baseline, 3 weeks, and 6 weeks and provided urine samples at baseline and at the 6-week follow-up. Complete data were available for 222 participants at week 3 and 186 participants at week 6.

Measures

Urinary 8-OHdG

As part of the SMART study, urine samples were collected from participants at baseline and at 6 weeks. Participants provided the baseline urine sample immediately after their team intake appointment, which occurred between 11 a.m. and 3 p.m. on the day they were enrolled in the study. The post-treatment urine sample was collected in person at MAMC upon completion of stage 2 of the SMART intervention. Urine samples were collected midstream in preservative-free containers and refrigerated until processing. Samples were first centrifuged at 200 ×g for 10 min to remove cells. The supernatant was then transferred to new tubes and centrifuged again at 1800 ×g for 10 min to remove debris. Subsequently, supernatants were transferred to clean tubes and stored at −80°C until the analyses. Urinary 8-OHdG levels were measured using a commercially available enzyme-linked immunosorbent assay (ELISA) kit from Novus Biologicals (Centennial, CO) following the manufacturer’s instructions. The 8-OHdG levels were normalized to creatinine, which was also measured by ELISA (RayBiotech, Peachtree Corners; GA).²⁴

Self-reported outcomes: Pain Assessment Screening Tool and Outcomes Registry (PASTOR)

Each participant completed PASTOR surveys at baseline, 3 weeks, and 6 weeks. PASTOR is a comprehensive web-based assessment system developed for the US Military Health System to evaluate and track chronic pain.²⁵ It combines standard National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) computerized adaptive tests with military-specific measures to provide a detailed, multidimensional pain profile. In addition, it includes tools such as the Defense and Veterans Pain Rating Scale; pain location mapping; and measures for pain interference, physical function, sleep disturbance, fatigue, emotional distress (depression, anxiety, and anger), social role participation, fear avoidance, post-traumatic stress, mild traumatic brain injury, opioid risk, and substance misuse. For this study, the primary outcomes we used from the participants’ PASTOR data were the PROMIS sleep-related function (computed as 100 minus the sleep impairment score) and PROMIS physical unction measures.

Objective measures: Treadmill, lift, and carry battery (TLC-bat)

Participants also underwent clinician-observed functional testing with TLC-bat, which consists of a graded treadmill test, floor-to-waist lift, waist-to-shoulder lift, and 40-foot carry.²⁶ Each component of the TLC-bat continues to build in difficulty until the participant’s pain intensity increases. The study assessed participants with the TLC-bat at baseline, 3 weeks, and 6 weeks.

Statistical analysis

We estimated partial correlations between changes in the 8-OHdG levels and changes in sleep quality. Separately, we estimated partial correlations between changes in 8-OHdG levels and changes in functional performance (objective and subjective). We adjusted the partial correlations for baseline 8-OHdG levels and, in separate models, for participant demographics. We added interaction terms to linear models to test for potential differential relationships based on participant intervention group (i.e. CIH or SRC treatment). We used linear mixed effect models, accounting for the repeated-measures nature of the data in analyses that pooled baseline, week 3, and week 6 data. Given the preliminary and exploratory nature of the study, we used a significance threshold of P <0.10 for all analyses, which provides 80% power for detecting true correlations of ≥0.18.

Results

Participant demographics

Table 1 provides the demographic distribution of participants with week 3 and week 6 data. Overall, little difference was observed between the two groups, and the small number of participants with week 3 but not week 6 data was fairly uniformly distributed; however, the percentage of participants aged 18–24 years was slightly lower after 6 weeks. The majority of participants were males; approximately half were <35 years old, approximately 30% were non-White, and approximately 14% were Hispanic. Most participants reported having a partner (up to 70.4%), only 25% reported an income more than US$50,000/year, and approximately 25% had only a high school diploma or general equivalency diploma (GED).

Table 1.

Participant demographic characteristics (n = 222).

Demographic characteristics	Week 3 (n = 222),% (n)	Week 6 (n = 186),% (n)
Sex–male	79.3% (176)	78.5% (146)
Age (years)
18–24	9.0% (20)	5.9% (11)
25–34	45.5% (101)	46.8% (87)
35–44	36.9% (82)	39.2% (73)
45–64	8.1% (18)	7.5% (14)
Race
Asian	12.2% (27)	11.3% (21)
Black	21.2% (47)	21.5% (40)
White	63.1% (140)	62.9% (117)
Other	3.6% (8)	4.3% (8)
Hispanic–yes	14.0% (31)	14.0% (26)
Income (per year)
<US$20,000	31.1% (69)	30.1% (56)
US$20,000–US$49,999	41.4% (92)	40.9% (76)
US$50,000–US$99,999	14.4% (32)	18.3% (34)
US$100,000+	7.2% (16)	5.9% (11)
Education completed
High school/GED	24.8% (55)	24.7% (46)
Some college/ Technical degree/ Associate’s degree	40.5% (90)	38.7% (72)
College degree (BA/BS)	23.0% (51)	23.7% (44)
Advanced degree (MA/PhD/MD)	8.6% (19)	10.2% (19)
Partnered–yes	69.4% (154)	70.4% (131)
Current tobacco use–yes	15.8% (35)	14.5% (27)

GED: general equivalency diploma; PhD: doctor of philosophy; BA: bachelor of arts; BS: bachelor of science; MA: masters of arts; MD: doctor of medicine.

Changes over time

The mean 8-OHdG level was slightly higher at the 6-week follow-up (mean = 10.77, SD = 4.3) than at baseline (mean = 10.58, SD = 4.4); however, this difference was not statistically significant (mean difference = 0.19, P = 0.51). Relatedly, after 6 weeks of intervention, similar numbers of individuals with higher 8-OHdG (n = 121) and lower 8-OHdG (n = 101) levels were observed compared with baseline (Figure 1). No evidence of significant differential change was observed by treatment group (SRC vs. CIH; P = .75).

Figure 1.

Distribution of oxidative stress (8-OHdG) levels over time (n = 222). Boxes represent the interquartile range, the center lines in the boxes indicate the median, whiskers extend to 1.5 × interquartile range, and dots indicate outliers. Pre-intervention and post-intervention distributions represent the values at each time point, and the Post–Predistribution represents the change value calculated as post-intervention minus pre-intervention. 8-OHdG: 8-hydroxy-2ʹ-deoxyguanosine.

Participants’ sleep functioning score as well as self-reported and objective measures of physical functioning showed improvements at both 3 and 6 weeks. Details are presented in Supplemental Table 1.

Associations between changes in oxidative stress and outcomes

Changes in sleep-related functioning at 3 and 6 weeks and in the pooled sample (week 3 and week 6 combined) were associated with a change in the 8-OHdG levels (i.e. oxidative stress). The strength of the association slightly increased after adjusting for demographic covariates. Partial correlations ranged from −0.17 to −0.24, suggesting that decreases in oxidative stress were associated with improvements in sleep-related functioning.

The correlation between the change in 8-OHdG levels at 6 weeks and improvement in the composite, objective measure of physical function at 3 weeks was statistically significant (partial correlation = −0.15; suggesting that decreases in oxidative stress were associated with improved objective physical functioning). However, this association was not statistically significant at week 6 (r = −0.05) or in the pooled sample (r = −0.08). Approximately all the objective secondary measures of physical functioning showed similar directionality, although none attained statistical significance. Finally, self-reported physical functioning demonstrated a similar pattern to the objective measure of physical function but never attained statistical significance. Details are presented in Table 2 and Figure 2. Additionally, scatter plots illustrating the associations between changes in oxidative stress and outcome measures are provided in Supplemental Figures 1–6.

Table 2.

Associations between changes in oxidative stress and changes in sleep and physical function measures (n = 222).

	Changes at week 3, partial correlation (90% CI) (n = 222)		Changes at week 6, partial correlation (90% CI) (n = 186)		Pooled sample, partial correlation (90% CI)
	Unadj	Adj^a	Unadj	Adj^a	Unadj	Adj^a
Primary outcomes
PROMIS sleep-related function	−0.09 (−0.2 to 0.02)	−0.17 (−0.3 to −0.03)^c	−0.18 (−0.3 to −0.06)^c	−0.24 (−0.38 to −0.1)^d	−0.13 (−0.22 to −0.03)^c	−0.19 (−0.3 to −0.08)^c
PROMIS physical function	0 (−0.11 to 0.12)	−0.01 (−0.14 to 0.12)	−0.05 (−0.17 to 0.07)	−0.03 (−0.18 to 0.11)	−0.02 (−0.12 to 0.08)	−0.02 (−0.13 to 0.1)
TLC-bat (composite score)	−0.1 (−0.21 to 0.01)	−0.15 (−0.28 to −0.01)^b	0 (−0.12 to 0.12)	−0.05 (−0.19 to 0.09)	−0.04 (−0.14 to 0.06)	−0.08 (−0.19 to 0.03)
Secondary outcomes
Treadmill (min)	−0.09 (−0.2 to 0.02)	−0.06 (−0.19 to 0.07)	−0.01 (−0.13 to 0.11)	−0.03 (−0.17 to 0.11)	−0.04 (−0.14 to 0.06)	−0.04 (−0.15 to 0.07)
Floor-to-waist lift (lbs)	−0.05 (−0.16 to 0.06)	−0.1 (−0.23 to 0.04)	0 (−0.12 to 0.12)	−0.06 (−0.2 to 0.08)	−0.01 (−0.11 to 0.08)	−0.07 (−0.18 to 0.04)
Waist-to-shoulder lift (lbs)	−0.09 (−0.2 to 0.02)	−0.12 (−0.26 to 0.01)	0.01 (−0.11 to 0.14)	0.01 (−0.13 to 0.15)	−0.04 (−0.14 to 0.06)	−0.06 (−0.17 to 0.06)
40-foot carry (lbs)	−0.05 (−0.16 to 0.06)	−0.12 (−0.26 to 0.01)	0 (−0.12 to 0.13)	−0.05 (−0.19 to 0.1)	−0.01 (−0.11 to 0.09)	−0.07 (−0.18 to 0.04)

Adjusted for baseline oxidative stress, intervention, and demographic variables (age, sex, income, race, ethnicity, tobacco use, education, and marital status).

P < 0.10; ^cP < 0.05; ^dP < 0.01.

Adj: adjusted; CI: confidence interval; PROMIS: Patient-Reported Outcomes Measurement Information System; TLC-bat: treadmill, lift, and carry battery; Unadj: unadjusted.

Figure 2.

Adjusted partial correlations between changes in oxidative stress and changes in sleep and physical function outcomes, with 90% confidence intervals. PROMIS: Patient-Reported Outcomes Measurement Information System; TLC-bat: treadmill, lift, and carry battery.

Meanwhile, there was no evidence that any of these relationships were moderated by the type of treatment an individual received (i.e. CIH vs. SRC; see Supplemental Table 2), as all tests for interaction yielded P values >0.05. Cross-sectional associations of oxidative stress and all outcome measures were also not statistically significant (Supplemental Table 3).

Discussion

This study examined the relationship between changes in urinary 8-OHdG levels and changes in physical function and sleep-related function among ADSM. Our findings suggest an association between oxidative stress and objective physical function and sleep-related function after adjusting for baseline 8-OHdG levels and other demographic variables. Therefore, 8-OHdG may serve as an objective marker of physical function and sleep-related function for adults, including ADSM, with chronic pain.

Changes in sleep-related function at both 3- and 6-week time points, as well as in the pooled sample from both time intervals, were associated with 6-week changes in oxidative stress. This finding is consistent with previous studies demonstrating a correlation between oxidative stress and sleep impairment.²⁷ Notably, the strength of these associations became more pronounced after adjusting for demographic covariates such as age, sex, and race, indicating that the relationship between sleep and oxidative stress appears robust and not solely explained by individual demographic differences.

Given that sleep restriction elevates endothelial oxidative stress and impairs antioxidant responses,²⁸ it is more plausible that the 8-OHdG levels reflect underlying sleep conditions than vice versa. In addition, our findings underscore the physiological relevance of sleep quality in modulating oxidative stress pathways over relatively short intervention periods. Meanwhile, other recent studies have implicated both oxidative stress and sleep impairment in increased pain intensity and sensitivity.^12,29,30 Therefore, 8-OHdG may serve as a valuable objective biomarker of sleep quality, with the potential to contribute to improved assessment and management of chronic pain.

Changes in the composite, objective measure of physical functioning at the 6-week mark did not demonstrate a statistically significant association with the 6-week change in oxidative stress. This suggests that although reductions in oxidative stress were modestly associated with improvements in objective physical functioning during the initial phase of the study, this relationship weakened over time. These findings may indicate that the effect of oxidative stress on objective physical functioning is more immediate or short-term, or that other factors play a greater role in influencing physical function over time. Another possible explanation for these findings is that improvements in physical function enabled participants to engage in more intense physical activity, which, in turn, could have elevated their levels of oxidative stress^31,32 and thereby obscured the underlying associations. In this context, 8-OHdG may be particularly useful as a short-term rather than long-term predictor of physical function, providing timely insight into early physiological responses to intervention.

Interestingly, we found no statistically significant associations between the changes in the 6-week oxidative stress levels and patient-reported assessments of physical functioning at the 3- and 6-week time points, despite the objective measures of physical functioning demonstrating a statistically significant association with changes in oxidative stress. This discrepancy highlights a potential divergence between self-perceived and objectively measured outcomes in evaluating treatment effects. It underscores the importance of incorporating objective biomarkers alongside self-reported measures to assess physical performance.

To promote the health and readiness of ADSM, understanding the interplay among physical function, sleep, and oxidative stress is essential. Although military personnel generally perceive themselves to be in good health and exhibit lower rates of chronic medical conditions such as obesity, hypertension, and diabetes compared than the general US population,³³ they are frequently exposed to high physical and psychological demands that can disrupt sleep and impair physical performance. By studying these associations, researchers and military health professionals can gain deeper insights into the physiological burden of service-related stressors. This understanding may support the development of targeted interventions, such as sleep optimization programs and performance recovery protocols, aimed at reducing oxidative stress and improving resilience. Ultimately, integrating these findings into military wellness strategies can enhance operational readiness, reduce injury and illness rates, and support the long-term well-being of service members.

Limitations

This study has several limitations. First, given the preliminary nature of the study, we used an exploratory significance threshold, which may have increased false positive findings. Therefore, these findings should be validated in other cohorts. Second, although we controlled for demographic variables in the analysis, unmeasured confounders such as smoking behavior and working environments³⁴ may have influenced the observed associations, particularly 8-OHdG levels. Third, the population included in the study was predominantly male, which limits the generalizability of the findings. Finally, our study does not support a causal relationship among oxidative stress, sleep, and physical function. Future studies involving larger and more demographically diverse samples and including a broader range of relevant variables is warranted to enhance the generalizability and robustness of the present findings.

Conclusions

Our findings suggest that 8-OHdG is a biomarker reflecting sleep quality and objective physical function among ADSM with chronic pain. Ultimately, this objective measure may be useful in the clinical environment by permitting medical teams to modify therapeutic strategies to provide more patient-tailored care.

Footnotes

Acknowledgments

We would like to thank all active duty service members who participated in this research.

Author contributions

T.J.S., S.S., and D.W. led the manuscript preparation, contributed to data analysis, interpreted the results, and drafted the original manuscript. N.T. and N.I. co-contributed to the statistical analyses and provided critical reviews and edits to the manuscript. J.C.R. and H.M.M. provided critical reviews and edits to the manuscript. D.M.F. and A.Z.D. conceptualized the study, supervised the project, interpreted the results, and provided critical reviews and edits to the manuscript. All authors read and approved the final manuscript.

Data availability statement

Data are available upon request to the corresponding author

Declaration of conflicting interests

The authors have no conflict of interest to report

Disclaimer

The US Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014 is the awarding and administering acquisition office. This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense through the Neuromusculoskeletal Injuries Rehabilitation Research Award (Award No. W81XWH-18-2-0023). Opinions, interpretations, conclusions, and recommendations are those of the authors and do not necessarily reflect the official policy or position or are endorsed by the Department of the Army, Department of Defense, National Institute of Health, or the US Government. The investigators adhered to the policies for protection of human participants as prescribed in 45 CFR 46.

Funding

This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense through the Neuromusculoskeletal Injuries Rehabilitation Research Award (Award No. W81XWH-18-2-0023) and the National Institute of Health/National Institute of Neurological Disorders and Stroke (K24 AT011995). No authors have any financial relationships or conflicts of interest to disclose.

Supplemental material

Supplemental material for this article is available online.

ORCID iD

Ardith Z Doorenbos

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